Langfuse AI employee
A Superkind AI employee that checks traces, explains failed generations, analyses scores, and documents incidents in Langfuse. Message it in Teams or Outlook, it works in Langfuse and reports back with the result. Sensitive actions wait for your approval.
- Hosted in the EU
- GDPR data processing agreement
- Ready to start today
- Works in Teams, Slack and more
What is a Langfuse AI employee?
A Langfuse AI employee connects to your Langfuse account and completes work in it. It checks traces and observations, explains failed generations, analyses scores, and documents incidents. Superkind builds it with your company knowledge, quality rules, and technical context.
Unlike Zapier or Make, there is nothing to configure. You describe the outcome in plain English, the AI employee picks the right Langfuse actions, connects them with Teams, Jira, or Linear, and asks for your approval before sensitive changes.
Langfuse is a platform for LLM observability, prompt management, evaluations, datasets, and experiments.
You ask in Teams
Describe in plain English which error or quality problem you want to investigate.
Superkind picks the actions
Selects the right Langfuse actions and connects them with your other systems.
Superkind works in Langfuse
Checks traces, observations, scores, and prompts using your real data.
Superkind reports back
Delivers the cause, evidence, and next steps in Teams or Outlook.
What can you ask Superkind to do in Langfuse?
Simply write what you want checked or completed, and Superkind takes it from there.
“Check today’s deployment in Langfuse and show me every failed trace with their common cause.”
you, to @Superkind“Use the observations to explain why checkout_agent has been timing out more often since this morning.”
you, to @Superkind“Create a Jira ticket for this error with affected generations, release, and your root cause analysis.”
you, to @Superkind“Let me know here as soon as a new incident appears in Langfuse, with trace, score, and likely cause.”
you, to @SuperkindHow does Superkind work with Langfuse?
- Native integrations and connectors for 1,000+ tools
1Connect your systems
Connect Langfuse with the required API keys. Add Teams and the systems your team already uses. Superkind configures access with minimum permissions. Your AI employee is then available in Teams.
@Superkind check the failed traces in today’s release, explain the cause, and create a score for the incident.
On it. I am checking the traces and observations in Langfuse and creating the score.
2Tell Superkind what you need
Message it in Teams like a colleague and say which traces or scores you want checked. Superkind understands your context and picks the right Langfuse actions. It can connect results with Jira or Linear. You do not need to build a workflow.
- 12 release traces checked
- Error: checkout_agent timeout
- Score latency_regression created
Score latency_regressionWaiting for your approvalMove prompt label to production?3Superkind operates, you approve
Superkind works in Langfuse and reports the cause, evidence, and next steps back in Teams. Read only checks run independently. Changes to prompts, datasets, or access wait for your approval. Every step is logged.
What can Superkind do in Langfuse?
Ask in plain English from Teams or Outlook. Superkind picks the right Langfuse actions, runs the work, and reports back. No workflows to build.
Search observations
Finds spans, generations, and events by time range, name, level, environment, or trace ID.
Reconstruct trace
Combines the observations for a trace ID into a complete execution path.
Check generations
Checks generations for errors, latency, model, token usage, and cost.
Analyse sessions
Groups traces by session ID and explains recurring problems across a conversation.
Explain error cause
Compares failed observations and identifies shared status messages, models, or releases.
Aggregate costs
Aggregates Langfuse costs by model, trace name, release, or time range.
Compare latency
Compares latency and time to first token across models, releases, or environments.
Analyse token usage
Shows input, output, and total token usage for selected Langfuse dimensions.
Measure trace volume
Measures trace volume by application, user, model, or time range.
Search scores
Finds scores by name, data type, source, value, environment, or time range.
Explain score trends
Explains changes in quality values across releases, models, or prompt versions.
Create score
Creates a numeric, boolean, categorical, or text score on a trace or observation.
Check score configs
Lists score configs with data type, categories, and allowed values.
List prompts
Lists Langfuse prompts with type, version, labels, and tags.
Get prompt version
Fetches a specific prompt version or the version behind a label.
Compare prompt impact
Compares scores, cost, and latency across linked prompt versions.
Create prompt version
Creates a new Langfuse prompt version with config, labels, and tags.
List datasets
Lists datasets with description, metadata, and creation date.
Check dataset items
Reads inputs, expected outputs, metadata, and status of individual dataset items.
Create dataset
Creates a Langfuse dataset for repeatable evaluations.
Create dataset item
Adds an item with input, expected output, and metadata to a dataset.
Compare experiments
Compares experiment runs using their items, scores, cost, and latency.
Find experiment failures
Finds experiment items with divergent outputs or low scores.
Check project access
Checks Langfuse project roles and permissions without changing access.
Invite member
Invites a member to the Langfuse organisation with a defined role.
Delete project
Deletes a Langfuse project with its traces, scores, prompts, and datasets.
Superkind AI employees that work with Langfuse
Every role brings its expertise and uses Langfuse as one of its tools.
Companies working with Superkind
Frequently asked questions
Everything you need to know about your AI employee for Langfuse.
Yes. Superkind connects to your Langfuse project through a managed connector. Your AI employee can then check traces, observations, metrics, scores, prompts, datasets, and experiments from Teams or Outlook. It receives only the agreed permissions and reports results back with the relevant evidence from Langfuse.
An admin provides the Langfuse API credentials for the selected project. Superkind configures the connector, tests the connection, and limits permissions to the agreed tasks. You can then reach your AI employee in Teams or Outlook. We test a real query together before your team starts using the connection.
It can search observations, reconstruct traces, explain failed generations, and analyse metrics for cost, tokens, or latency. It can also check scores, compare prompt versions, read datasets, and summarise experiment runs. Write actions such as creating scores or dataset items only run according to the approval rules you define.
No. With Zapier or Make, you build and maintain triggers, filters, and individual steps. With Superkind, you simply describe the desired outcome in Teams or Outlook. The AI employee picks the right Langfuse actions itself, connects them with GitHub, Jira, or Linear when needed, and asks when information is missing.
Only with your approval. Reading traces, comparing scores, and explaining errors can run independently. New prompt versions, dataset items, members, or deleting a project wait for an explicit yes from a responsible person in Teams. Your team decides together with Superkind which Langfuse actions count as sensitive.
Superkind is hosted in the EU and signs a GDPR data processing agreement with you. The connector uses minimum Langfuse permissions for the agreed tasks. Your data is not used for model training. Access and completed actions are logged so you can trace which traces, scores, prompts, or datasets the AI employee processed.
Langfuse offers a free starting tier. Paid cloud plans begin in roughly the low double digits per month, while broader plans sit in the low hundreds of euros per month. Zapier starts at about 20 euros and Make at about 10 euros per month, both plus setup time. Superkind is priced per use case, a fraction of a full-time hire.